Add Guardrails So Your AI App Doesn't Lie — A Two-Layer Approach with NVIDIA NIM
The article discusses a two-layer approach to adding guardrails to AI applications using NVIDIA NIM. It emphasizes the importance of preventing AI models from generating incorrect or out-of-scope responses. The author outlines methods to tighten prompts and implement grounding checks to ensure accurate and relevant answers.
- ▪The first layer involves tightening the prompt to define what the AI assistant can discuss.
- ▪The second layer includes a grounding check that verifies if the answer is supported by the context before delivering it.
- ▪The article highlights two common failure modes in AI responses: out-of-scope creep and confident-sounding hallucinations.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3943111) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Torkian Posted on May 24 Add Guardrails So Your AI App Doesn't Lie — A Two-Layer Approach with NVIDIA NIM #nvidia #ai #python #tutorial NVIDIA NIM from First Call to Working Agent (3 Part Series) 1 Build Your First AI App with NVIDIA NIM in 30 Minutes 2 From Manual RAG to Real Retrieval — Embedding-Based RAG with NVIDIA NIM 3 Add Guardrails So Your AI App Doesn't Lie — A Two-Layer Approach with NVIDIA NIM In Part 1 we got a USC campus assistant talking.
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